An integrated method for identifying essential proteins from multiplex network model of protein-protein interactions
Cell survival requires the presence of essential proteins. Detection of essential proteins is relevant not only because of the critical biological functions they perform but also the role played by them as a drug target against pathogens. Several computational techniques are in place to identify essential proteins based on protein-protein interaction (PPI) network. Essential protein detection using only physical interaction data of proteins is challenging due to its inherent uncertainty. Hence, in this work, we propose a multiplex network-based framework that incorporates multiple protein interaction data from their physical, coexpression and phylogenetic profiles. An extended version termed as multiplex eigenvector centrality (MEC) is used to identify essential proteins from this network. The methodology integrates the score obtained from the multiplex analysis with subcellular localization and Gene Ontology information and is implemented using Saccharomyces cerevisiae datasets. The proposed method outperformed many recent essential protein prediction techniques in the literature.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
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| Enthalten in: |
Journal of bioinformatics and computational biology - 18(2020), 4 vom: 01. Aug., Seite 2050020 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Athira, K [VerfasserIn] |
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| Links: |
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| Themen: |
Eigenvector centrality |
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| Anmerkungen: |
Date Completed 23.08.2021 Date Revised 23.08.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1142/S0219720020500201 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM313697930 |
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| 520 | |a Cell survival requires the presence of essential proteins. Detection of essential proteins is relevant not only because of the critical biological functions they perform but also the role played by them as a drug target against pathogens. Several computational techniques are in place to identify essential proteins based on protein-protein interaction (PPI) network. Essential protein detection using only physical interaction data of proteins is challenging due to its inherent uncertainty. Hence, in this work, we propose a multiplex network-based framework that incorporates multiple protein interaction data from their physical, coexpression and phylogenetic profiles. An extended version termed as multiplex eigenvector centrality (MEC) is used to identify essential proteins from this network. The methodology integrates the score obtained from the multiplex analysis with subcellular localization and Gene Ontology information and is implemented using Saccharomyces cerevisiae datasets. The proposed method outperformed many recent essential protein prediction techniques in the literature | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a eigenvector centrality | |
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| 700 | 1 | |a Gopakumar, G |e verfasserin |4 aut | |
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